Mohammad Mahdi Ershadi | Biomedical Applications | Innovative Research Award

Innovative Research Award

    Mohammad Mahdi Ershadi
Affiliation Amirkabir University of Technology
Country Iran
Scopus ID 57212585059
Documents 22
Citations 196
h-index 10
Subject Area Biomedical Applications
Event Global Tech Excellence Awards
ORCID 0000-0002-7409-6469

Mohammad Mahdi Ershadi, affiliated with Amirkabir University of Technology, has established an emerging research profile in biomedical applications through interdisciplinary studies involving medical imaging, machine learning, and healthcare data analytics. His publications demonstrate contributions toward artificial intelligence methods for clinical decision support and image analysis while maintaining active scholarly engagement within internationally indexed research platforms.[1]

Abstract

Mohammad Mahdi Ershadi has developed a focused research portfolio in biomedical applications by integrating artificial intelligence, medical image analysis, and healthcare data interpretation. His publications investigate advanced segmentation methods, ensemble learning strategies, and data quality assessment for clinical decision support. Indexed scholarly outputs, measurable citation performance, and interdisciplinary collaboration collectively demonstrate a growing academic influence. These contributions support innovation in healthcare technologies while reflecting scientific rigor, reproducibility, and practical relevance. The documented research achievements indicate meaningful progress toward improving computational biomedical systems and advancing evidence-based medical analytics through modern intelligent computing methodologies.[1][2][3]

Keywords

Biomedical Applications, Artificial Intelligence, Medical Imaging, Chest X-ray Analysis, Machine Learning, Deep Learning, Ensemble Learning, Image Segmentation, Healthcare Analytics, Clinical Decision Support.

Introduction

The research activities of Mohammad Mahdi Ershadi emphasize computational intelligence for biomedical applications, particularly medical imaging and healthcare analytics. His interdisciplinary investigations combine artificial intelligence with clinical datasets to improve diagnostic reliability, segmentation accuracy, and decision-support methodologies, contributing practical scientific value within contemporary biomedical engineering research.[1]

Research Profile

According to indexed academic records, the researcher has authored twenty-two scholarly documents, received one hundred ninety-six citations, and achieved an h-index of ten. These indicators demonstrate sustained publication activity and increasing scholarly recognition within biomedical applications, machine learning, and computational healthcare research communities internationally.[1]

Research Contributions

Major research contributions include intelligent chest X-ray segmentation, ensemble learning for respiratory disease diagnosis, and investigations into data quality metadata supporting evidence-based decision making. These studies integrate advanced machine learning algorithms with healthcare applications, encouraging accurate medical interpretation and computational innovation across biomedical environments.[1][2]

Publications

The publication portfolio reflects consistent contributions to internationally recognized journals and scholarly platforms focusing on artificial intelligence, medical image processing, and biomedical engineering. Research outputs demonstrate methodological development, experimental validation, and practical healthcare relevance, supporting continuous academic advancement through peer-reviewed scientific dissemination.[1][3]

Research Impact

Citation metrics, interdisciplinary collaborations, and practical biomedical applications collectively indicate growing research impact. The published studies support advancements in healthcare technologies through robust computational methods, while influencing ongoing investigations involving medical image interpretation, clinical analytics, and intelligent diagnostic systems across international scientific communities.[1][2]

Award Suitability

Considering documented publication performance, measurable citation indicators, interdisciplinary biomedical research, and internationally indexed scholarly contributions, Mohammad Mahdi Ershadi demonstrates qualifications consistent with recognition under the Innovative Research Award. His scientific achievements illustrate meaningful advancement of intelligent healthcare technologies through rigorous academic investigation and innovation.[1][3]

Conclusion

Mohammad Mahdi Ershadi has established an emerging academic profile characterized by interdisciplinary biomedical research, measurable scholarly impact, and contributions to artificial intelligence for healthcare. Continued publication activity and collaborative scientific engagement are expected to strengthen future influence while supporting innovations addressing contemporary medical and computational challenges.[1][2][3]

References

  1. Ershadi, M. M., et al. (2026). Entropy-guided semi-supervised framework for robust chest X-ray segmentation using dynamic competition and patch-wise contrastive learning. Biomedical Signal Processing and Control.
    https://www.sciencedirect.com/science/article/abs/pii/S1746809426004878?via%3Dihub
  2. Ershadi, M. M., et al. (2025). Decoding DQM for Experimental Insights on Data Quality Metadata’s Impact on Decision-Making Process Efficacy.
    https://www.scopus.com/pages/publications/105023471399
  3. Ershadi, M. M., et al. (2025). Application of Ensemble Learning for Respiratory Ailment Diagnosis: Case Studies on Biomedical and Chest X-ray Image Datasets. Qeios.
    https://www.qeios.com/read/1NMNYE.3
  4. Elsevier. (n.d.). Scopus Author Details: Mohammad Mahdi Ershadi, Author ID 57212585059. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57212585059

Ateke Goshvarpour | Biomedical and Healthcare Applications | Editorial Board Member

Assist. Prof. Dr. Ateke Goshvarpour | Biomedical and Healthcare Applications | Editorial Board Member

Assistant Professor | Imam Reza International University | Iran

Dr. Ateke Goshvarpour, affiliated with Imam Reza International University, Mashhad, Iran, is a distinguished researcher specializing in biomedical signal processing, cognitive neuroscience, and computational modeling of brain activity. With a prolific research portfolio comprising 70 publications and over 1,095 citations across 727 scholarly documents, Dr. Goshvarpour has established a strong global reputation for her contributions to the understanding and classification of cognitive and mental disorders using advanced signal analysis techniques.Her recent works focus on EEG-based diagnosis of schizophrenia, emotion recognition, and cognitive assessment, integrating concepts from quantum-inspired computation, chaotic dynamics, and neural connectivity analysis. Notable studies such as “Enhancing Schizophrenia Diagnosis through EEG Frequency Waves and Information-Based Neural Connectivity Feature Fusion” and “Quantum-Inspired Feature Extraction Model for Enhanced Schizophrenia Detection” highlight her innovative approach in bridging neuroscience with machine learning and chaos theory. Through the development of spectral–spatiotemporal models and graph-based signal representations, she provides novel pathways for noninvasive brain disorder diagnostics and affective computing.Collaborating with a network of 21 co-authors, Dr. Goshvarpour demonstrates an interdisciplinary outlook, integrating engineering, data science, and psychology to improve diagnostic precision and healthcare outcomes. Her h-index of 20 reflects both the impact and consistency of her research influence. Beyond academia, her work contributes significantly to societal well-being by enabling early and accurate detection of neurological conditions and enhancing emotional intelligence systems.Dr. Goshvarpour’s dedication to advancing the frontier of biomedical and cognitive signal processing underscores her role as a leading figure in computational neuroscience research, fostering a deeper understanding of human cognition through data-driven and bio-inspired intelligence frameworks.

Profiles: ORCID |  Scopus | Google Scholar

Featured Publications

1.Goshvarpour, A. (2025). Enhancing schizophrenia diagnosis through EEG frequency waves and information-based neural connectivity feature fusion. Biomedical Signal Processing and Control.

2.Goshvarpour, A. (2025). Quantum-inspired feature extraction model from EEG frequency waves for enhanced schizophrenia detection. Chaos, Solitons & Fractals. Cited By : 1

3.Goshvarpour, A. (2025). Cognitive-inspired spectral spatiotemporal analysis for emotion recognition utilizing electroencephalography signals. Cognitive Computation. Cited By : 4

4.Goshvarpour, A. (2025). Asymmetric measures of polar Chebyshev chaotic map for discrete/dimensional emotion recognition using PPG. Biomedical Signal Processing and Control. Cited By : 1

5.Goshvarpour, A. (2025). Diagnosis of cognitive and mental disorders: A new approach based on spectral–spatiotemporal analysis and local graph structures of electroencephalogram signals. Brain Sciences. Cited By : 3

Dr. Ateke Goshvarpour’s pioneering research in biomedical signal processing and neurocomputational modeling is transforming the early detection of mental and cognitive disorders. By integrating EEG analytics, chaos theory, and AI-driven methods, her work bridges neuroscience and technology—advancing precision diagnostics, enhancing emotional intelligence systems, and fostering global innovation in digital health and mental well-being.

Arsene Jaures Ouemba Tasse | Biomedical and Healthcare Applications | Best Researcher Award

Dr . Arsene Jaures Ouemba Tasse | Biomedical and Healthcare Applications | Best Researcher Award

Postdoc at University of the Witwatersrand, Johannesburg, South Africa

Dr. Arsène Jaures Ouemba Tasse is a dynamic Postdoctoral Research Fellow at the University of the Witwatersrand, South Africa, with a specialized focus in applied dynamical systems and mathematical modeling of infectious diseases. He holds a Ph.D. in Mathematics from the University of Dschang, Cameroon, and has published extensively in high-impact journals on topics including Ebola, COVID-19, Typhoid, and Monkeypox. His research contributions have global significance, particularly in understanding disease transmission dynamics and control strategies. Dr. Ouemba Tasse has participated in numerous international conferences, received prestigious research grants, and supervised both undergraduate and postgraduate students. He is also an active reviewer for several scientific journals and has contributed to collaborative projects funded by renowned institutions like the Bill & Melinda Gates Foundation. His growing leadership in academic events, combined with his commitment to public health through mathematics, positions him as a highly influential figure in the field of mathematical epidemiology.

Professional Profile 

Education🎓 

Dr. Arsène Jaures Ouemba Tasse has a strong academic foundation in mathematics, beginning with a Bachelor’s degree from the University of Yaoundé I in 2009. He pursued his Honours and Master’s degrees in Mathematics at the University of Dschang, Cameroon, where he graduated with distinction. His academic journey culminated in earning a Ph.D. in Applied Dynamical Systems and Mathematical Modeling from the same institution in 2021. His doctoral studies equipped him with advanced knowledge in differential equations, epidemiological modeling, partial differential equations, and numerical analysis. Additionally, he obtained a Secondary and High School Teacher’s Diploma in Mathematics from the Higher Teacher’s Training College of Yaoundé in 2008, highlighting his early commitment to education. To enhance his international research engagement, he completed an English language course at the University of the Witwatersrand in 2023. His academic pathway reflects both depth and breadth in mathematical sciences, with a strong emphasis on applied research for real-world impact.

Professional Experience📝

Dr. Ouemba Tasse brings over 15 years of professional experience in teaching, research, and academic mentorship. Since November 2022, he has served as a Postdoctoral Research Fellow at the University of the Witwatersrand, Johannesburg, where he engages in cutting-edge research, student supervision, and academic event organization. Before this, he spent over a decade teaching mathematics in various high schools in Cameroon, including the General High School Tsela and the General Bilingual High School Bameka. His university-level experience includes lecturing and tutoring in courses such as algebra, calculus, mathematical modeling, and discrete population dynamics. He has participated in academic panels, supervised postgraduate research groups, and served as an external examiner. His professional journey reflects a seamless transition from secondary education to advanced research, demonstrating versatility, leadership, and commitment to educational excellence in both national and international academic environments.

Research Interest🔎

Dr. Ouemba Tasse’s research interests are centered around mathematical epidemiology, applied dynamical systems, and optimal control theory, particularly in the context of infectious disease modeling. He focuses on developing and analyzing mathematical models that simulate the transmission dynamics of diseases such as Ebola, COVID-19, Typhoid, Malaria, Monkeypox, and HIV. His models incorporate various control strategies including awareness programs, vaccination, isolation, and traditional versus modern treatment methods. He also works on the mathematical formulation and numerical solutions using nonstandard finite difference schemes that ensure stability and accuracy in epidemic simulations. His recent projects explore the co-dynamics of multiple infections and the role of environmental and behavioral factors in disease propagation. Additionally, he is interested in the intersection of public health and mathematics, including modeling cancer progression and mother-to-child HIV transmission. His interdisciplinary approach bridges mathematical theory and health policy, offering vital insights for effective disease control and healthcare intervention strategies.

Award and Honor🏆

Dr. Ouemba Tasse has received numerous accolades and support from prestigious institutions for his impactful research in mathematical modeling. He was awarded a Postdoctoral Fellowship by the University of the Witwatersrand, along with funding from the Bill & Melinda Gates Foundation for a cervical cancer modeling project. He received full sponsorships from the Simons Foundation and the Pacific Institute of Mathematical Sciences to attend international conferences and research schools in Canada and the USA. He won first prize in Analysis at a postgraduate workshop in Cameroon and was a recipient of the Humboldt Foundation’s funding during the Dschang Humboldt Kolleg. His academic excellence has been further recognized through grant awards from the Society for Mathematical Biology and participation in global conferences like BIOMATH and CIMPA. These honors not only acknowledge his research excellence but also reflect his growing reputation as a leading contributor to mathematical modeling in epidemiology and public health.

Research Skill🔬

Dr. Ouemba Tasse possesses advanced research skills in both theoretical and computational aspects of applied mathematics. He is proficient in developing deterministic and stochastic models of infectious diseases, applying optimal control techniques, and performing stability analysis of equilibrium states. His expertise in nonstandard finite difference schemes enhances the accuracy and robustness of numerical simulations. He is well-versed in software tools such as MATLAB, MATHEMATICA, R, and LaTeX, which he uses extensively for simulations, data analysis, and scientific writing. His research includes data fitting and parameter estimation, and he has applied these techniques in real-world epidemiological studies. He also has strong collaborative skills, having led and co-supervised numerous multidisciplinary projects and study groups. Additionally, he contributes as a peer reviewer for reputed journals and book chapters, showcasing his analytical precision and subject-matter authority. These combined skills make him an adept and resourceful researcher capable of addressing complex public health challenges through mathematics.

Conclusion💡

Dr. Arsène Jaures Ouemba Tasse is a highly promising and competitive candidate for the Best Researcher Award, especially in fields involving epidemiological modeling, applied mathematics, and computational public health. His international exposure, robust publication record, academic mentoring, and societal relevance of his research make him exceptionally well-qualified for this honor.

While still in an early career stage, his trajectory shows exemplary leadership potential and deep scholarly contributions. With minor improvements such as increasing PI roles and broader interdisciplinary outreach, he would not only be eligible but also a standout award recipient.

Publications Top Noted✍

  1. Title: Mathematical modeling of contact tracing as a control strategy of Ebola virus disease
    Authors: T. Berge, A.J. Ouemba Tassé, H.M. Tenkam, J. Lubuma
    Year: 2018
    Citations: 34

  2. Title: Dynamics of host-reservoir transmission of Ebola with spillover potential to humans
    Authors: B. Tsanou, J.M.S. Lubuma, A.J.O. Tassé, H.M. Tenkam
    Year: 2018
    Citations: 22

  3. Title: Ebola virus disease dynamics with some preventive measures: a case study of the 2018–2020 Kivu outbreak
    Authors: A.J. Ouemba Tasse, B. Tsanou, J. Lubuma, J.L. Woukeng, F. Signing
    Year: 2022
    Citations: 6

  4. Title: Nonstandard finite difference schemes for some epidemic optimal control problems
    Authors: A.J.O. Tassé, V.B. Kubalasa, B. Tsanou
    Year: 2025
    Citations: 2

  5. Title: A metapopulation model with exit screening measure for the 2014–2016 West Africa Ebola virus outbreak
    Authors: A.J.O. Tassé, B. Tsanou, J.L. Woukeng, J.M.S. Lubuma
    Year: 2024
    Citations: 1

  6. Title: A mathematical model to study herbal and modern treatments against COVID-19
    Authors: A.J. Ouemba Tassé, B. Tsanou, C. Kwa Kum, J. Lubuma
    Year: 2024
    Citations: 1

  7. Title: Assessment of effective isolation, safe burial and vaccination optimal controls for an Ebola epidemic model
    Authors: A.J.O. Tassé, B. Tsanou, J.M.S. Lubuma, J.L. Woukeng
    Year: 2020
    Citations: 1

  8. Title: Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon
    Authors: T.J. Tsafack, C.K. Kum, A.J.O. Tassé, B. Tsanou
    Year: 2025

  9. Title: A mathematical model on the impact of awareness and traditional medicine in the control of Ebola: case study of the 2014–2016 outbreaks in Sierra Leone and Liberia
    Authors: A.J. Ouemba Tassé, B. Tsanou, C.K. Kum, J. Lubuma
    Year: 2024

  10. Title: Influence of the co-dynamics Ebola-COVID-19 in the population
    Authors: A.J.O. Tassé, J. Lubuma, B. Tsanou
    Year: 2023

  11. Title: Investigating the impact of isolation, self-isolation and environmental transmission on the spread of COVID-19: case study in Rwanda
    Authors: J.M.S. Lubuma, A.J.O. Tassé, F. Signing, B. Tsanou
    Year: 2023

  12. Title: Modélisation mathématique de la transmission de la maladie à virus Ebola et stratégies de contrôle
    Authors: A.J.O. Tasse
    Year: 2021

  13. Title: Mathematical modeling of contact tracing as a control strategy of Ebola virus disease (Duplicate entry)
    Authors: B. Tsanou, A.J. Ouemba Tassé, H.M. Tenkam, J.M.S. Lubuma
    Year: 2018

  14. Title: Investigating the impact of isolation, self-isolation and environmental transmission on the spread of COVID-19: case study of Rwanda (Duplicate entry)
    Authors: M.S.L. Jean, A.J.O. Tassé, F. Signing, B. Tsanou
    Year: 2023